1. Field of the Invention
The present invention relates to the technical field of image processing and, more particularly, to a method and system for enhancing image sharpness based on local features of image.
2. Description of Related Art
The sharp change in edges and/or grey scales of an image mostly corresponds to the high frequency components of the image. A high-pass filter is typically used to enhance image sharpness. Namely, with the high pass filter, the low frequency components of the image are attenuated without interfering in the high frequency components.
However, as the use of high pass filter only may attenuate the low frequency components of the image, it leads to an image distortion. To overcome this, prior art uses an unsharp masking to enhance the image sharpness, and in this case the unsharp masking subtracts an unclear version from the original image to thereby obtain a sharpened image. The unsharp masking can be expressed as follows.fS(x,y)=f(x,y)− f(x,y),  (1)where f(x, y) indicates an original image or an image before the unsharp masking, f(x,y) indicates an unclear version of the original image, and fS(x,y) indicates an image after the unsharp masking.
A high-boosting filtering is a next generation of the unsharp masking. The high-boosting filtering image is defined as:fhb(x,y)=A×f(x,y)− f(x,y),  (2)where A is greater than or equal to one, f(x,y) indicates an original image or an image before the unsharp masking, f(x,y) indicates an unclear version of the original image, and fhb(x,y) indicates a high-boosting filtered image. The high-boosting filtered image fhb(x,y) can be rewritten as:fhb(x,y)=(A−1)×f(x,y)+f(x,y)− f(x,y).  (3)
From equation (3), equation (1) can be rewritten as:fhb(x,y)=(A−1)×f(x,y)+fS(x,y).  (4)In this case, the high boosting filtering can be implemented by the masks in FIG. 1. When A=1, the high boosting filtering is similar to a Laplacian sharpening. When A is greater than one and increased more and more, the contribution of sharpening process is decreased less and less. When A is sufficiently big, the high-boosting filtered image can be considered as a value of the original image multiplying a constant.
No matter for the Laplacian sharpening, the unsharp masking, or the high-boosting filtering, it requires nine multipliers and eight adders for performing a masking operation, which costs very high in hardware and does not meet with the practical requirement.
Therefore, it is desirable to provide an improved method and system for enhancing image sharpness based on local features of image to mitigate and/or obviate the aforementioned problems.